A natural scene usually contains some smoothly changing areas where luminance levels are changing gradually. When such a scene is captured by a digitization device into a digitized image, the luminance value for each pixel in the smoothly changing areas is quantized by an analog-to-digital (A/D). As a result, the digitized image may contain quantization artifacts such as stage-like false contours. Because quantization artifacts are perceivable by the human eye, the smoothly changing areas in the natural scene no longer look smooth in the digitized image.
The magnitude of quantization artifacts is determined by the quantization accuracy in the A/D converter of the digitization device. This magnitude is not necessarily the same as the smallest step of the digitization device. Indeed, it is usually much larger and makes the quantization artifacts more perceivable to human eyes.
To eliminate these types of quantization artifacts, first their location must be identified, and then smoothing processes performed thereon. In general, within a large slowly changing region the quantization artifacts resemble steps. However, identifying quantization artifacts in a digitized image of a natural scene is a difficult task. This is because such identification requires detecting whether the quantization artifacts are caused by the quantization of smoothly changing areas in the original scene, or if they are actual features in the original scene itself.
The presence of additive noise introduced by the digitization device makes such a detection more complicated. This is because additive noise makes the areas containing quantization artifacts look as small detailed regions in the original scene. If a noisy area is detected as an area that contains quantization artifacts, the smoothing process must reduce the noise, as well as the image quantization layer (or bit-plane).
Further, a smoothly changing area may look stage-like even when the luminance of neighboring pixels is only changing by the smallest possible step. In this case, a higher precision content of the smoothly changing area is desired in order to eliminate the quantization artifacts. With the higher precision content, halftoning techniques such as error diffusion or spatial dithering can be used to quantize the higher precision content to the current bit depth. The quantization artifacts will no longer be perceivable on the halftoned image due to the spatial averaging characteristics of the human visual system. There is therefore, a need for a method and a system for reducing the quantization layer in a quantized image.